By using blockchain, FLchain can attract more devices in the learning process for better scalability thanks to its decentralized network topology. Particularly, the information on learning updates
is secure in a form of immutable blocks which improves the security of FL training in edge networks [1]}. With its decentralized and secure nature, FLchain is promising to provide attractive solutions for enabling intelligent and scalable edge networks with security guarantees. The comparison between blackthe classical FL and FLchain is summarized in Table REF . In addition to benefits, FLchain still remains some drawbacks such as possible latency from blockchain mining, resource management, and security from curious miners, which need to be considered in system designs. In this article, we perform a holistic investigation and discussion of related issues in FLchain design via some key use cases, which will be presented in detail in Section .
